HuggingFace + Ray AIR Integration: A Python Developer’s Guide to Scaling Transformers

Hugging Face Transformers is a popular open-source project with cutting-edge Machine Learning (ML). Still, meeting the computational requirements for advanced models it provides often requires scaling beyond a single machine. This session explores the integration between Hugging Face and Ray AI Runtime (AIR), allowing users to scale their model training and data loading seamlessly. We will dive deep into the implementation and API and explore how we can use Ray AIR to create an end-to-end Hugging Face workflow, from data ingest through fine-tuning and HPO to inference and serving.
 
 The computational and memory requirements for fine-tuning and training these models can be significant. To deal with this issue, the Ray team has developed a Hugging Face integration for Ray AI Runtime (AIR), allowing Transformers model training to be easily parallelized across multiple CPUs or GPUs in a Ray Cluster, saving time and money, all the while allowing to take advantage of the rich Ray ML ecosystem thanks to standard and common API.
 
In this session, we explore the integration between Hugging Face and Ray AIR, allowing users to scale their model training and data loading seamlessly. We will dive deep into the implementation and API and explore how we can use Ray AIR to create an end-to-end Hugging Face workflow, from data ingest through fine-tuning and HPO to inference and serving. 

 Key Takeaways: 

•   Python developers and machine learning engineers can use * 
•  Transformers and scale their language models.
•  Get exposed to Ray AIR’s Python APIs for end-to-end Hugging Face and ML workflow.
•  Understand how Ray AIR, built atop Ray, can scale your Python-based ML workloads.